Abstract
DNA methylation is involved in many different biological processes in the development and well-being of crop plants such as transposon activation, heterosis, environment-dependent transcriptome plasticity, aging, and many diseases. Whole-genome bisulfite sequencing is an excellent technology for detecting and quantifying DNA methylation patterns in a wide variety of species, but optimized data analysis pipelines exist only for a small number of species and are missing for many important crop plants. This is especially important as most existing benchmark studies have been performed on mammals with hardly any repetitive elements and without CHG and CHH methylation. Pipelines for the analysis of whole-genome bisulfite sequencing data usually consists of four steps: read trimming, read mapping, quantification of methylation levels, and prediction of differentially methylated regions (DMRs). Here we focus on read mapping, which is challenging because un-methylated cytosines are transformed to uracil during bisulfite treatment and to thymine during the subsequent polymerase chain reaction, and read mappers must be capable of dealing with this cytosine/thymine polymorphism. Several read mappers have been developed over the last years, with different strengths and weaknesses, but their performances have not been critically evaluated. Here, we compare eight read mappers: Bismark, BismarkBwt2, BSMAP, BS-Seeker2, Bwameth, GEM3, Segemehl, and GSNAP to assess the impact of the read-mapping results on the prediction of DMRs. We used simulated data generated from the genomes of Arabidopsis thaliana, Brassica napus, Glycine max, Solanum tuberosum, and Zea mays, monitored the effects of the bisulfite conversion rate, the sequencing error rate, the maximum number of allowed mismatches, as well as the genome structure and size, and calculated precision, number of uniquely mapped reads, distribution of the mapped reads, run time, and memory consumption as features for benchmarking the eight read mappers mentioned above. Furthermore, we validated our findings using real-world data of Glycine max and showed the influence of the mapping step on DMR calling in WGBS pipelines. We found that the conversion rate had only a minor impact on the mapping quality and the number of uniquely mapped reads, whereas the error rate and the maximum number of allowed mismatches had a strong impact and leads to differences of the performance of the eight read mappers. In conclusion, we recommend BSMAP which needs the shortest run time and yields the highest precision, and Bismark which requires the smallest amount of memory and yields precision and high numbers of uniquely mapped reads.
Highlights
It has been shown that DNA methylation is involved in several biological mechanisms and diseases such as cancer (Koch et al, 2018)
The memory consumption ranged from 0.1 GB for the mapping of the Arabidopsis thaliana dataset with Bismark, either using bowtie or bowtie2, to 39 GB for the mapping of the Zea mays dataset with Segemehl
The user time is depicted, ranging from a few minutes for all mappers using the Arabidopsis thaliana dataset, to 79 h for the mapping of a Zea mays dataset with Segemehl
Summary
It has been shown that DNA methylation is involved in several biological mechanisms and diseases such as cancer (Koch et al, 2018). Plant methylation analysis is especially interesting as 5-methyl-cytosine (5mC) is involved in the heterosis effect (Chen et al, 2018), transposon silencing, and environment-dependent transcriptome plasticity (Lauss et al, 2018). Whole-genome bisulfite sequencing (WGBS) is often referred to as the “gold standard” for 5mC detection because the whole genome is covered at a single-base resolution. Other methods cover only preselected genome regions enriched for cytosinephosphate-guanine-dinucleotide (CpG) content or methylation, for example with the use of restriction enzymes in reduced representation bisulfite sequencing (rrBS) (Sun et al, 2015), or methylated DNA immune precipitation, followed by generation sequencing (MeDIP-seq) (Bock et al, 2010; Aberg et al, 2017). Methylated cytosines remain unaffected and could be spotted by alignment of the generated sequencing reads to a reference genome or a non-bisulfitetreated control
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